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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

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        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.29

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        MultiQC_changed_max_bin_size

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/mag analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-10-23, 11:57 UTC based on data in: /mnt/batch/tasks/workitems/job-6365fd9ac5db59386ed9-NFCORE_MAG_MAG_MULTIQC/job-1/nf-eaa8f86c3d8ea2a53f4fbdf28376a4f5/wd

        General Statistics

        Showing 102/102 rows and 16/29 columns.
        Sample Name% Dups (raw)% GC (raw)Avg. length (raw)Median length (raw)M Seqs (raw)% Fails (raw)% Dups (processed)% GC (processed)Avg. length (processed)Median length (processed)M Seqs (processed)% Fails (processed)% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% Adapter% Aligned (Host)% Aligned (Assem.)N50 (Kbp)N50 (Kbp)Assembly Length (Mbp)Assembly Length (Mbp)ContigsBasesCDSOrganism
        MEGAHIT-CONCOCT-group-0_0
        1.4Kbp
        0.0Mbp
        4
        5804
        9
        Genus species
        MEGAHIT-CONCOCT-group-0_1
        13.9Kbp
        0.0Mbp
        4
        34852
        31
        Genus species
        MEGAHIT-CONCOCT-group-0_2
        2.6Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_3
        1.5Kbp
        0.0Mbp
        1
        1452
        Genus species
        MEGAHIT-CONCOCT-group-0_4
        2.1Kbp
        0.0Mbp
        2
        3351
        7
        Genus species
        MEGAHIT-CONCOCT-group-0_5
        1.2Kbp
        0.0Mbp
        1
        1165
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_6
        5.1Kbp
        0.1Mbp
        27
        99345
        92
        Genus species
        MEGAHIT-CONCOCT-group-0_8
        1.4Kbp
        0.0Mbp
        2
        2408
        6
        Genus species
        MEGAHIT-CONCOCT-group-0_9
        1.1Kbp
        0.0Mbp
        1
        1103
        Genus species
        MEGAHIT-CONCOCT-group-0_10
        1.7Kbp
        0.0Mbp
        2
        2690
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_11
        1.0Kbp
        0.0Mbp
        1
        1034
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_12
        1.2Kbp
        0.0Mbp
        2
        2211
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_13
        27.3Kbp
        3.2Mbp
        171
        3159141
        2995
        Genus species
        MEGAHIT-CONCOCT-group-0_14
        2.1Kbp
        0.0Mbp
        10
        20970
        23
        Genus species
        MEGAHIT-CONCOCT-group-0_15
        2.5Kbp
        0.0Mbp
        1
        2522
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_16
        1.0Kbp
        0.0Mbp
        1
        1021
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_17
        1.2Kbp
        0.0Mbp
        2
        2317
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_18
        1.0Kbp
        0.0Mbp
        1
        1006
        Genus species
        MEGAHIT-CONCOCT-group-0_19
        1.5Kbp
        0.0Mbp
        1
        1451
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_20
        3.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_21
        1.2Kbp
        0.0Mbp
        1
        1222
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_22
        1.3Kbp
        0.0Mbp
        3
        3600
        3
        Genus species
        MEGAHIT-CONCOCT-group-0_23
        1.3Kbp
        0.3Mbp
        215
        299242
        294
        Genus species
        MEGAHIT-CONCOCT-group-0_24
        2.5Kbp
        0.0Mbp
        2
        3834
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_25
        1.2Kbp
        0.0Mbp
        1
        1151
        Genus species
        MEGAHIT-CONCOCT-group-0_26
        1.1Kbp
        0.0Mbp
        1
        1142
        Genus species
        MEGAHIT-CONCOCT-group-0_27
        1.3Kbp
        0.0Mbp
        2
        2323
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_28
        2.4Kbp
        0.1Mbp
        32
        67957
        84
        Genus species
        MEGAHIT-CONCOCT-group-0_29
        1.2Kbp
        0.0Mbp
        1
        1169
        Genus species
        MEGAHIT-CONCOCT-group-0_30
        3.0Kbp
        0.0Mbp
        1
        3036
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_31
        1.1Kbp
        0.0Mbp
        3
        3830
        6
        Genus species
        MEGAHIT-CONCOCT-group-0_33
        5.0Kbp
        0.2Mbp
        38
        153015
        155
        Genus species
        MEGAHIT-CONCOCT-group-0_34
        1.1Kbp
        0.0Mbp
        1
        1069
        Genus species
        MEGAHIT-CONCOCT-group-0_35
        2.1Kbp
        1.2Mbp
        600
        1153449
        928
        Genus species
        MEGAHIT-CONCOCT-group-0_36
        7.3Kbp
        0.0Mbp
        1
        7335
        4
        Genus species
        MEGAHIT-CONCOCT-group-0_37
        2.5Kbp
        0.1Mbp
        63
        138874
        141
        Genus species
        MEGAHIT-CONCOCT-group-0_38
        1.1Kbp
        0.0Mbp
        1
        1058
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_39
        2.4Kbp
        0.0Mbp
        3
        7734
        7
        Genus species
        MEGAHIT-CONCOCT-group-0_40
        81.8Kbp
        2.0Mbp
        51
        1980306
        1988
        Genus species
        MEGAHIT-CONCOCT-group-0_41
        1.1Kbp
        0.0Mbp
        1
        1113
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_42
        3.8Kbp
        0.0Mbp
        3
        6275
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_43
        1.1Kbp
        0.0Mbp
        1
        1137
        Genus species
        MEGAHIT-CONCOCT-group-0_44
        64.4Kbp
        0.1Mbp
        11
        105732
        47
        Genus species
        MEGAHIT-CONCOCT-group-0_45
        2.7Kbp
        0.0Mbp
        6
        10566
        11
        Genus species
        MEGAHIT-CONCOCT-group-0_46
        1.1Kbp
        0.0Mbp
        3
        3384
        3
        Genus species
        MEGAHIT-CONCOCT-group-0_47
        1.2Kbp
        0.0Mbp
        4
        4637
        6
        Genus species
        MEGAHIT-CONCOCT-group-0_48
        1.6Kbp
        0.0Mbp
        9
        13922
        17
        Genus species
        MEGAHIT-CONCOCT-group-0_49
        2.3Kbp
        0.0Mbp
        2
        4380
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_50
        2.8Kbp
        0.0Mbp
        1
        2818
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_51
        5.7Kbp
        0.9Mbp
        245
        907971
        981
        Genus species
        MEGAHIT-CONCOCT-group-0_52
        9.5Kbp
        0.2Mbp
        39
        200027
        218
        Genus species
        MEGAHIT-CONCOCT-group-0_53
        2.4Kbp
        0.0Mbp
        1
        2350
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_54
        1.4Kbp
        0.0Mbp
        28
        39020
        33
        Genus species
        MEGAHIT-CONCOCT-group-0_55
        47.6Kbp
        0.1Mbp
        5
        52634
        47
        Genus species
        MEGAHIT-CONCOCT-group-0_56
        1.2Kbp
        1.3Mbp
        1046
        1334114
        1064
        Genus species
        MEGAHIT-CONCOCT-group-0_57
        2.6Kbp
        1.8Mbp
        785
        1808459
        1656
        Genus species
        MEGAHIT-CONCOCT-group-0_58
        1.7Kbp
        0.0Mbp
        1
        1658
        3
        Genus species
        MEGAHIT-MaxBin2-group-0.001
        27.1Kbp
        2.2Mbp
        166
        2205317
        2096
        Genus species
        MEGAHIT-MaxBin2-group-0.002
        24.4Kbp
        1.2Mbp
        99
        1167014
        1129
        Genus species
        MEGAHIT-MaxBin2-group-0.004
        20.5Kbp
        0.1Mbp
        26
        144728
        142
        Genus species
        MEGAHIT-MetaBAT2-group-0.2
        17.0Kbp
        0.4Mbp
        22
        360483
        344
        Genus species
        MEGAHIT-MetaBAT2-group-0.3
        30.2Kbp
        0.8Mbp
        39
        769406
        710
        Genus species
        MEGAHIT-MetaBAT2-group-0.4
        3.6Kbp
        0.7Mbp
        209
        720875
        666
        Genus species
        MEGAHIT-MetaBAT2-group-0.5
        44.7Kbp
        0.8Mbp
        28
        778495
        742
        Genus species
        MEGAHIT-MetaBAT2-group-0.6
        91.2Kbp
        1.7Mbp
        30
        1703612
        1681
        Genus species
        MEGAHIT-MetaBAT2-group-0.7
        23.2Kbp
        1.1Mbp
        83
        1136911
        1085
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.1
        57.2Kbp
        0.1Mbp
        1
        57236
        50
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.2
        47.6Kbp
        0.0Mbp
        1
        47618
        39
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.3
        36.7Kbp
        0.0Mbp
        1
        36655
        47
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.4
        32.4Kbp
        0.0Mbp
        1
        32368
        29
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.5
        30.2Kbp
        0.0Mbp
        1
        30204
        25
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.6
        30.1Kbp
        0.0Mbp
        1
        30106
        35
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.7
        25.1Kbp
        0.0Mbp
        1
        25100
        25
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.8
        24.4Kbp
        0.0Mbp
        1
        24436
        35
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.9
        21.7Kbp
        0.0Mbp
        1
        21707
        19
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.10
        20.7Kbp
        0.0Mbp
        1
        20709
        19
        Genus species
        MEGAHIT-MetaBAT2-group-0.unbinned.11
        20.5Kbp
        0.0Mbp
        1
        20501
        19
        Genus species
        MEGAHIT-group-0
        21.5Kbp
        39.3Mbp
        MEGAHIT-group-0-O1
        99.1%
        MEGAHIT-group-0-O2
        98.8%
        MEGAHIT-group-0-O3
        98.8%
        MEGAHIT-group-0-O4
        99.3%
        O1_run0
        0.6%
        100.0%
        3613.0Mb
        24.1M
        43.3%
        100.0%
        0.4%
        0.1%
        O1_run0_raw_1
        45.7%
        43.0%
        150bp
        150bp
        12.0M
        0%
        O1_run0_raw_2
        46.1%
        43.0%
        150bp
        150bp
        12.0M
        0%
        O1_run0_trimmed_1
        45.7%
        43.0%
        150bp
        150bp
        12.0M
        0%
        O1_run0_trimmed_2
        46.1%
        43.0%
        150bp
        150bp
        12.0M
        0%
        O2_run0
        0.5%
        100.0%
        3626.2Mb
        24.2M
        42.9%
        100.0%
        0.6%
        0.2%
        O2_run0_raw_1
        41.7%
        42.0%
        150bp
        150bp
        12.1M
        0%
        O2_run0_raw_2
        42.2%
        42.0%
        150bp
        150bp
        12.1M
        0%
        O2_run0_trimmed_1
        41.8%
        42.0%
        150bp
        150bp
        12.1M
        0%
        O2_run0_trimmed_2
        42.2%
        42.0%
        150bp
        150bp
        12.1M
        0%
        O3_run0
        0.8%
        100.0%
        3644.9Mb
        24.3M
        44.2%
        100.0%
        0.5%
        0.0%
        O3_run0_raw_1
        56.8%
        44.0%
        150bp
        150bp
        12.2M
        10%
        O3_run0_raw_2
        57.4%
        44.0%
        150bp
        150bp
        12.2M
        10%
        O3_run0_trimmed_1
        56.8%
        44.0%
        150bp
        150bp
        12.2M
        10%
        O3_run0_trimmed_2
        57.4%
        44.0%
        150bp
        150bp
        12.2M
        10%
        O4_run0
        0.7%
        100.0%
        3618.8Mb
        24.1M
        44.3%
        100.0%
        0.3%
        0.0%
        O4_run0_raw_1
        57.4%
        44.0%
        150bp
        150bp
        12.1M
        10%
        O4_run0_raw_2
        58.2%
        44.0%
        150bp
        150bp
        12.1M
        10%
        O4_run0_trimmed_1
        57.4%
        44.0%
        150bp
        150bp
        12.1M
        10%
        O4_run0_trimmed_2
        58.2%
        44.0%
        150bp
        150bp
        12.1M
        10%

        FastQC: raw reads

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (150bp)

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        8 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        No samples found with any adapter contamination > 0.1%

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        fastp

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC


        FastQC: after preprocessing

        After trimming and, if requested, contamination removal.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        8 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        No samples found with any adapter contamination > 0.1%

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Bowtie2: host removal

        Mapping statistics of reads mapped against host genome and subsequently removed.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        QUAST: assembly

        Assembly statistics of raw assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 1/1 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-group-0
        21.5Kbp
        2.3Kbp
        0.3K
        1.7K
        391.7Kbp
        39.3Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        Bowtie2: assembly

        Mapping statistics of reads mapped against assemblies.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        QUAST: bins

        Assembly statistics of binned assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 77/77 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-CONCOCT-group-0_0
        1.4Kbp
        1.4Kbp
        0.0K
        0.0K
        2.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_1
        13.9Kbp
        13.9Kbp
        0.0K
        0.0K
        13.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_2
        2.6Kbp
        1.6Kbp
        0.0K
        0.0K
        5.6Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_3
        1.5Kbp
        1.5Kbp
        0.0K
        0.0K
        1.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_4
        2.1Kbp
        1.2Kbp
        0.0K
        0.0K
        2.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_5
        1.2Kbp
        1.2Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_6
        5.1Kbp
        2.5Kbp
        0.0K
        0.0K
        25.1Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_8
        1.4Kbp
        1.0Kbp
        0.0K
        0.0K
        1.4Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_9
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_10
        1.7Kbp
        1.0Kbp
        0.0K
        0.0K
        1.7Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_11
        1.0Kbp
        1.0Kbp
        0.0K
        0.0K
        1.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_12
        1.2Kbp
        1.0Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_13
        27.3Kbp
        15.9Kbp
        0.0K
        0.1K
        119.6Kbp
        3.2Mbp
        MEGAHIT-CONCOCT-group-0_14
        2.1Kbp
        1.5Kbp
        0.0K
        0.0K
        5.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_15
        2.5Kbp
        2.5Kbp
        0.0K
        0.0K
        2.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_16
        1.0Kbp
        1.0Kbp
        0.0K
        0.0K
        1.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_17
        1.2Kbp
        1.1Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_18
        1.0Kbp
        1.0Kbp
        0.0K
        0.0K
        1.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_19
        1.5Kbp
        1.5Kbp
        0.0K
        0.0K
        1.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_20
        3.1Kbp
        2.2Kbp
        0.0K
        0.0K
        7.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_21
        1.2Kbp
        1.2Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_22
        1.3Kbp
        1.0Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_23
        1.3Kbp
        1.1Kbp
        0.1K
        0.1K
        4.8Kbp
        0.3Mbp
        MEGAHIT-CONCOCT-group-0_24
        2.5Kbp
        1.3Kbp
        0.0K
        0.0K
        2.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_25
        1.2Kbp
        1.2Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_26
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_27
        1.3Kbp
        1.0Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_28
        2.4Kbp
        1.6Kbp
        0.0K
        0.0K
        6.0Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_29
        1.2Kbp
        1.2Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_30
        3.0Kbp
        3.0Kbp
        0.0K
        0.0K
        3.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_31
        1.1Kbp
        1.0Kbp
        0.0K
        0.0K
        1.7Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_33
        5.0Kbp
        3.1Kbp
        0.0K
        0.0K
        17.7Kbp
        0.2Mbp
        MEGAHIT-CONCOCT-group-0_34
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_35
        2.1Kbp
        1.5Kbp
        0.2K
        0.4K
        11.7Kbp
        1.2Mbp
        MEGAHIT-CONCOCT-group-0_36
        7.3Kbp
        7.3Kbp
        0.0K
        0.0K
        7.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_37
        2.5Kbp
        1.6Kbp
        0.0K
        0.0K
        7.3Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_38
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_39
        2.4Kbp
        2.4Kbp
        0.0K
        0.0K
        3.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_40
        81.8Kbp
        51.4Kbp
        0.0K
        0.0K
        214.1Kbp
        2.0Mbp
        MEGAHIT-CONCOCT-group-0_41
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_42
        3.8Kbp
        1.4Kbp
        0.0K
        0.0K
        3.8Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_43
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_44
        64.4Kbp
        8.6Kbp
        0.0K
        0.0K
        64.4Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_45
        2.7Kbp
        1.3Kbp
        0.0K
        0.0K
        2.7Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_46
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_47
        1.2Kbp
        1.1Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_48
        1.6Kbp
        1.2Kbp
        0.0K
        0.0K
        2.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_49
        2.3Kbp
        2.1Kbp
        0.0K
        0.0K
        2.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_50
        2.8Kbp
        2.8Kbp
        0.0K
        0.0K
        2.8Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_51
        5.7Kbp
        2.5Kbp
        0.0K
        0.1K
        36.7Kbp
        0.9Mbp
        MEGAHIT-CONCOCT-group-0_52
        9.5Kbp
        3.4Kbp
        0.0K
        0.0K
        57.2Kbp
        0.2Mbp
        MEGAHIT-CONCOCT-group-0_53
        2.4Kbp
        2.4Kbp
        0.0K
        0.0K
        2.4Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_54
        1.4Kbp
        1.2Kbp
        0.0K
        0.0K
        2.6Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_55
        47.6Kbp
        47.6Kbp
        0.0K
        0.0K
        47.6Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_56
        1.2Kbp
        1.1Kbp
        0.4K
        0.7K
        3.4Kbp
        1.3Mbp
        MEGAHIT-CONCOCT-group-0_57
        2.6Kbp
        1.7Kbp
        0.2K
        0.4K
        14.8Kbp
        1.8Mbp
        MEGAHIT-CONCOCT-group-0_58
        1.7Kbp
        1.7Kbp
        0.0K
        0.0K
        1.7Kbp
        0.0Mbp
        MEGAHIT-MaxBin2-group-0.001
        27.1Kbp
        14.3Kbp
        0.0K
        0.1K
        82.7Kbp
        2.2Mbp
        MEGAHIT-MaxBin2-group-0.002
        24.4Kbp
        12.5Kbp
        0.0K
        0.0K
        119.6Kbp
        1.2Mbp
        MEGAHIT-MaxBin2-group-0.004
        20.5Kbp
        3.5Kbp
        0.0K
        0.0K
        57.2Kbp
        0.1Mbp
        MEGAHIT-MetaBAT2-group-0.2
        17.0Kbp
        12.5Kbp
        0.0K
        0.0K
        67.0Kbp
        0.4Mbp
        MEGAHIT-MetaBAT2-group-0.3
        30.2Kbp
        20.2Kbp
        0.0K
        0.0K
        82.7Kbp
        0.8Mbp
        MEGAHIT-MetaBAT2-group-0.4
        3.6Kbp
        2.8Kbp
        0.1K
        0.1K
        14.8Kbp
        0.7Mbp
        MEGAHIT-MetaBAT2-group-0.5
        44.7Kbp
        22.0Kbp
        0.0K
        0.0K
        119.6Kbp
        0.8Mbp
        MEGAHIT-MetaBAT2-group-0.6
        91.2Kbp
        63.6Kbp
        0.0K
        0.0K
        214.1Kbp
        1.7Mbp
        MEGAHIT-MetaBAT2-group-0.7
        23.2Kbp
        12.2Kbp
        0.0K
        0.0K
        75.3Kbp
        1.1Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.1
        57.2Kbp
        57.2Kbp
        0.0K
        0.0K
        57.2Kbp
        0.1Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.2
        47.6Kbp
        47.6Kbp
        0.0K
        0.0K
        47.6Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.3
        36.7Kbp
        36.7Kbp
        0.0K
        0.0K
        36.7Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.4
        32.4Kbp
        32.4Kbp
        0.0K
        0.0K
        32.4Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.5
        30.2Kbp
        30.2Kbp
        0.0K
        0.0K
        30.2Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.6
        30.1Kbp
        30.1Kbp
        0.0K
        0.0K
        30.1Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.7
        25.1Kbp
        25.1Kbp
        0.0K
        0.0K
        25.1Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.8
        24.4Kbp
        24.4Kbp
        0.0K
        0.0K
        24.4Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.9
        21.7Kbp
        21.7Kbp
        0.0K
        0.0K
        21.7Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.10
        20.7Kbp
        20.7Kbp
        0.0K
        0.0K
        20.7Kbp
        0.0Mbp
        MEGAHIT-MetaBAT2-group-0.unbinned.11
        20.5Kbp
        20.5Kbp
        0.0K
        0.0K
        20.5Kbp
        0.0Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        CheckM

        Estimates genome completeness and contamination based on the presence or absence of marker genes.URL: https://github.com/Ecogenomics/CheckMDOI: 10.1101/gr.186072.114

        Bin quality

        The quality of microbial genomes recovered from isolates, single cells, and metagenomes.

        An automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes.

        Showing 74/74 rows and 6/6 columns.
        Bin IdMarker lineageGenomesMarkersMarker setsCompletenessContamination
        MEGAHIT-CONCOCT-group-0_0
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_1
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_3
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_4
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_5
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_6
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_8
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_9
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_10
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_11
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_12
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_13
        o__Lactobacillales (UID462)
        85
        367
        162
        98.56%
        2.93%
        MEGAHIT-CONCOCT-group-0_14
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_15
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_16
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_17
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_18
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_19
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_21
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_22
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_23
        k__Bacteria (UID203)
        5449
        104
        58
        4.91%
        0.00%
        MEGAHIT-CONCOCT-group-0_24
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_25
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_26
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_27
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_28
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_29
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_30
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_31
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_33
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_34
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_35
        k__Archaea (UID2)
        207
        149
        107
        9.78%
        0.93%
        MEGAHIT-CONCOCT-group-0_36
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_37
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_38
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_39
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_40
        f__Leuconostocaceae (UID486)
        29
        443
        178
        99.89%
        0.56%
        MEGAHIT-CONCOCT-group-0_41
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_42
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_44
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_45
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_46
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_47
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_48
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_49
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_50
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_51
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_52
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_53
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_54
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_55
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_56
        k__Archaea (UID2)
        207
        145
        103
        7.03%
        0.49%
        MEGAHIT-CONCOCT-group-0_57
        o__Lactobacillales (UID463)
        75
        376
        160
        59.94%
        0.98%
        MEGAHIT-CONCOCT-group-0_58
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MaxBin2-group-0.001
        o__Lactobacillales (UID374)
        471
        350
        191
        39.79%
        0.79%
        MEGAHIT-MaxBin2-group-0.002
        o__Lactobacillales (UID462)
        85
        367
        162
        59.47%
        1.47%
        MEGAHIT-MaxBin2-group-0.004
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.2
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.3
        o__Lactobacillales (UID374)
        471
        350
        191
        19.37%
        0.52%
        MEGAHIT-MetaBAT2-group-0.4
        root (UID1)
        5656
        56
        24
        8.33%
        0.00%
        MEGAHIT-MetaBAT2-group-0.5
        k__Bacteria (UID203)
        5449
        104
        58
        56.90%
        0.00%
        MEGAHIT-MetaBAT2-group-0.6
        f__Leuconostocaceae (UID486)
        29
        443
        178
        99.89%
        0.19%
        MEGAHIT-MetaBAT2-group-0.7
        k__Bacteria (UID203)
        5449
        103
        58
        13.95%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.1
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.2
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.3
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.4
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.5
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.6
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.7
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.8
        k__Bacteria (UID203)
        5449
        104
        58
        5.17%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.9
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.10
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.unbinned.11
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%

        Prokka

        Rapid annotation of prokaryotic genomes.URL: http://www.vicbioinformatics.com/software.prokka.shtmlDOI: 10.1093/bioinformatics/btu153

        This barplot shows the distribution of different types of features found in each contig.

        Prokka can detect different features:

        • CDS
        • rRNA
        • tmRNA
        • tRNA
        • miscRNA
        • signal peptides
        • CRISPR arrays

        This barplot shows you the distribution of these different types of features found in each contig.

        Created with MultiQC

        GTDB-Tk

        Assigns objective taxonomic classifications to bacterial and archaeal genomes.URL: https://ecogenomics.github.io/GTDBTk/index.htmlDOI: 10.1093/bioinformatics/btac672

        MAG taxonomy

        The taxonomy of a MAG as found by GTDB.

        GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB. It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes.

        Showing 7/7 rows and 6/8 columns.
        User genomeClassificationFull classificationClassification methodANI to closest genomeAF to closest genomeREDWarningsNotes
        MEGAHIT-CONCOCT-group-0_13.fa
        s__Lactiplantibacillus plantarum
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum
        ani_screen
        99.0
        0.9
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_40.fa
        s__Leuconostoc citreum
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Leuconostoc; s__Leuconostoc citreum
        ani_screen
        99.0
        0.9
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_57.fa
        s__Levilactobacillus brevis
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Levilactobacillus; s__Levilactobacillus brevis
        ani_screen
        98.8
        0.9
        classification based on ANI only
        MEGAHIT-MaxBin2-group-0.001.fa
        s__Lactiplantibacillus plantarum
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum
        ani_screen
        99.0
        0.9
        classification based on ANI only
        MEGAHIT-MaxBin2-group-0.002.fa
        s__Lactiplantibacillus plantarum
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum
        ani_screen
        98.8
        0.8
        classification based on ANI only
        MEGAHIT-MetaBAT2-group-0.5.fa
        s__Lactiplantibacillus plantarum
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Lactiplantibacillus; s__Lactiplantibacillus plantarum
        ani_screen
        99.2
        1.0
        classification based on ANI only
        MEGAHIT-MetaBAT2-group-0.6.fa
        s__Leuconostoc citreum
        d__Bacteria; p__Bacillota; c__Bacilli; o__Lactobacillales; f__Lactobacillaceae; g__Leuconostoc; s__Leuconostoc citreum
        ani_screen
        99.0
        0.9
        classification based on ANI only

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        ADJUST_MAXBIN2_EXTcoreutils9.5
        BIN_SUMMARYpandas1.4.3
        python3.10.6
        BOWTIE2_ASSEMBLY_ALIGNbowtie22.4.2
        pigz2.3.4
        samtools1.11
        BOWTIE2_ASSEMBLY_BUILDbowtie22.4.2
        BOWTIE2_HOST_REMOVAL_ALIGNbowtie22.4.2
        BOWTIE2_HOST_REMOVAL_BUILDbowtie22.4.2
        CHECKM_LINEAGEWFcheckm1.2.3
        CHECKM_QAcheckm1.2.3
        CONCAT_BINQC_TSVcsvtk0.31.0
        CONCOCT_CONCOCTconcoct1.1.0
        CONCOCT_CONCOCTCOVERAGETABLEconcoct1.1.0
        CONCOCT_CUTUPFASTAconcoct1.1.0
        CONCOCT_EXTRACTFASTABINSconcoct1.1.0
        CONCOCT_MERGECUTUPCLUSTERINGconcoct1.1.0
        CONVERT_DEPTHSbioawk20110810
        DASTOOL_DASTOOLdastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_CONCOCTdastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_MAXBIN2dastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_METABAT2dastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_TIARAdastool1.1.7
        FASTQC_RAWfastqc0.12.1
        FASTQC_TRIMMEDfastqc0.12.1
        GENOMAD_ENDTOENDgenomad1.11.0
        GTDBTK_CLASSIFYWFgtdb_dbr226
        gtdbtk2.5.2
        GTDBTK_SUMMARYpandas1.4.3
        python3.10.6
        GUNZIP_BINSgunzip1.13
        GUNZIP_SHORTREAD_ASSEMBLIESgunzip1.13
        GUNZIP_UNBINSgunzip1.13
        MAG_DEPTHSpandas1.1.5
        python3.6.7
        MAG_DEPTHS_PLOTpandas1.3.0
        python3.9.6
        seaborn0.11.0
        MAG_DEPTHS_SUMMARYpandas1.4.3
        python3.10.6
        MAXBIN2maxbin22.2.7
        MEGAHITmegahit1.2.9
        METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS_SHORTREADmetabat22.15
        METABAT2_METABAT2metabat22.17
        PRODIGALpigz2.6
        prodigal2.6.3
        Prokkaprokka1.14.6
        QUASTmetaquast5.0.2
        python3.7.6
        QUAST_BINSmetaquast5.0.2
        python3.7.6
        QUAST_BINS_SUMMARYcp9.5
        RENAME_POSTDASTOOLcoreutils9.5
        RENAME_PREDASTOOLcoreutils9.5
        SEQKIT_STATSseqkit2.9.0
        SPLIT_FASTAbiopython1.7.4
        pandas1.1.5
        python3.6.7
        TIARA_SUMMARYcsvtk0.31.0
        TIARA_TIARAtiara1.0.3
        WorkflowNextflow25.04.8
        nf-core/magv5.0.0-g3d41222
        fastpfastp0.24.0
        r-baser-base4.1.3
        r-tidyverser-tidyverse1.3.1

        nf-core/mag Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/mag

        Methods

        Data was processed using nf-core/mag v5.0.0 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.8 (Di Tommaso et al., 2017) with the following command:

        nextflow run 'https://github.com/nf-core/mag' -name anna_mags_orange_v13_3 -params-file 'https://api.cloud.seqera.io/ephemeral/ggjOP4arJJd-c9Bt1A1Dzg.json' -with-tower -r 3d41222776d2ed7a78dd3fd4dd643690c49f6bd2 -resume 6146cb7d-c639-42e5-83bb-479b9eadec73

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/mag Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/nf-core/mag

        Input/output options

        email
        annalifousi@gmail.com
        input
        az://seqera/results/anna-mags/mag_samplesheet_orange_new.csv
        multiqc_title
        MultiQC_changed_max_bin_size
        outdir
        az://seqera/results/anna-mags/results_orange_new/

        Quality control for short reads options

        host_fasta
        az://seqera/raw/orange/orange_genome/GCF_022201045.2_DVS_A1.0_genomic.fna
        host_removal_save_ids
        true
        host_removal_verysensitive
        true
        keep_phix
        true
        save_clipped_reads
        true
        save_hostremoved_reads
        true

        Quality control for long reads options

        longreads_min_quality
        1

        Taxonomic profiling options

        cat_db
        az://seqera/databases/cat/20241212_CAT_nr_website/
        gtdb_db
        az://seqera/databases/gtdb/GTDB/gtdbtk_package/full_package/release226/
        gtdbtk_max_contamination
        10.0
        gtdbtk_min_completeness
        30
        gtdbtk_use_full_tree
        true

        Assembly options

        coassemble_group
        true
        skip_flye
        true
        skip_metamdbg
        true
        skip_spades
        true
        skip_spadeshybrid
        true

        Gene prediction and annotation options

        skip_metaeuk
        true

        Virus identification options

        genomad_db
        az://seqera/databases/geNomadDB/14886553/genomad_db/
        run_virus_identification
        true

        Binning options

        bin_domain_classification
        true
        bin_max_size
        10000000
        min_length_unbinned_contigs
        20000
        save_assembly_mapped_reads
        true

        Bin quality check options

        binqc_tool
        checkm
        busco_db_lineage
        N/A
        checkm_db
        az://seqera/databases/checkm_data/
        gunc_save_db
        true
        refine_bins_dastool
        true

        Core Nextflow options

        configFiles
        /.nextflow/assets/nf-core/mag/nextflow.config, /mnt/batch/tasks/workitems/nf-workflow-43P98YrW5SZ1j/job-1/nf-workflow-43P98YrW5SZ1j/wd/nextflow.config
        launchDir
        /mnt/batch/tasks/workitems/nf-workflow-43P98YrW5SZ1j/job-1/nf-workflow-43P98YrW5SZ1j/wd
        profile
        standard
        projectDir
        /.nextflow/assets/nf-core/mag
        revision
        5.0.0
        runName
        anna_mags_orange_v13_3
        userName
        root
        workDir
        /seqera/results/anna-mags/work